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            <h1 id="seo-header">『机器学习』人工智能考题可能性猜测</h1>
            
            
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                <h1 id="『机器学习』人工智能考题可能性猜测"><a href="#『机器学习』人工智能考题可能性猜测" class="headerlink" title="『机器学习』人工智能考题可能性猜测"></a>『机器学习』人工智能考题可能性猜测</h1><h1 id="选择、填空"><a href="#选择、填空" class="headerlink" title="选择、填空"></a>选择、填空</h1><h4 id="人工智能的提出"><a href="#人工智能的提出" class="headerlink" title="人工智能的提出"></a>人工智能的提出</h4><p>1956 年，达特茅斯会议上，麦卡锡 提出了“AI”</p>
<p>“AI 之父”：麦卡锡</p>
<p>第一个人工智能测试系统：图灵测试</p>
<h4 id="推理过程"><a href="#推理过程" class="headerlink" title="推理过程"></a>推理过程</h4><p>医疗专家系统（<strong>综合数据库、知识库、推理机</strong>）</p>
<h4 id="归结"><a href="#归结" class="headerlink" title="归结"></a>归结</h4><p>$c_{1}$ 和$c_{2}$是$c_{12}$的<strong>亲本子句</strong></p>
<h4 id="状态空间（搜索策略中的知识表示方法）"><a href="#状态空间（搜索策略中的知识表示方法）" class="headerlink" title="状态空间（搜索策略中的知识表示方法）"></a>状态空间（搜索策略中的知识表示方法）</h4><p>利用&#x3D;&#x3D;状态变量&#x3D;&#x3D;和&#x3D;&#x3D;操作符号&#x3D;&#x3D;，表示系统问题或问题的有关知识的符号体系，状态空间是一个四元组，（S,O,$S_0$,G）</p>
<h4 id="遗传算法"><a href="#遗传算法" class="headerlink" title="遗传算法"></a>遗传算法</h4><p>生物学基础是<strong>生物进化理论</strong></p>
<p><strong>Holland</strong> 提出了遗传算法</p>
<table>
<thead>
<tr>
<th>生物遗传概念</th>
<th>遗传算法应用</th>
</tr>
</thead>
<tbody><tr>
<td>个体</td>
<td>解</td>
</tr>
<tr>
<td>染色体</td>
<td>解的编码</td>
</tr>
<tr>
<td>基因</td>
<td>编码中的每一个分量</td>
</tr>
<tr>
<td>适应性</td>
<td>适应度函数值</td>
</tr>
<tr>
<td>群体</td>
<td>一组解</td>
</tr>
</tbody></table>
<h4 id="kNN-中-k-的含义"><a href="#kNN-中-k-的含义" class="headerlink" title="kNN 中 k 的含义"></a>kNN 中 k 的含义</h4><p>选择 k 个与判别值最近邻的值</p>
<h4 id="分类模型学习算法"><a href="#分类模型学习算法" class="headerlink" title="分类模型学习算法"></a>分类模型学习算法</h4><ul>
<li>SVN</li>
<li>kNN</li>
<li>DT</li>
<li>ANN</li>
</ul>
<h1 id="简答题"><a href="#简答题" class="headerlink" title="简答题"></a>简答题</h1><h4 id="人工智能的概念"><a href="#人工智能的概念" class="headerlink" title="人工智能的概念"></a>人工智能的概念</h4><p>用人工的方法在机器上实现的智能</p>
<h4 id="智能的特征"><a href="#智能的特征" class="headerlink" title="智能的特征"></a>智能的特征</h4><ul>
<li>感知能力</li>
<li>记忆与思维能力</li>
<li>学习能力</li>
<li>行为能力</li>
</ul>
<h4 id="思维的种类"><a href="#思维的种类" class="headerlink" title="思维的种类"></a>思维的种类</h4><ul>
<li>逻辑思维（抽象思维）</li>
<li>形象思维（直感思维）</li>
<li>顿悟思维（灵感思维）</li>
</ul>
<h4 id="三大主义学派——实现-AI-的主要途径"><a href="#三大主义学派——实现-AI-的主要途径" class="headerlink" title="三大主义学派——实现 AI 的主要途径"></a>三大主义学派——实现 AI 的主要途径</h4><h5 id="符号主义"><a href="#符号主义" class="headerlink" title="符号主义"></a>符号主义</h5><ul>
<li>基本思想<br>模拟人类的逻辑思维</li>
<li>主要代表成果<br>纽威尔和西蒙提出的“逻辑理论家 LT”数学定理证明程序</li>
</ul>
<h5 id="行为主义"><a href="#行为主义" class="headerlink" title="行为主义"></a>行为主义</h5><ul>
<li>基本思想<br>模拟生物进化</li>
<li>主要代表成果<br>布鲁克斯设计的“六足机器虫”</li>
</ul>
<h5 id="连接主义"><a href="#连接主义" class="headerlink" title="连接主义"></a>连接主义</h5><ul>
<li>基本思想<br>模拟人类的大脑结构</li>
<li>主要代表成果<br>各种人工神经网络算法<br>麦克洛奇和 PIZZ 的 MP 神经网络模型</li>
</ul>
<h4 id="为什么引入谓词逻辑（命题的缺点）"><a href="#为什么引入谓词逻辑（命题的缺点）" class="headerlink" title="为什么引入谓词逻辑（命题的缺点）"></a>为什么引入谓词逻辑（命题的缺点）</h4><p>无法把它所描述的&#x3D;&#x3D;事物结构&#x3D;&#x3D;及&#x3D;&#x3D;逻辑思维特征&#x3D;&#x3D;反映出来，也不能把&#x3D;&#x3D;不同事物间的共同特征&#x3D;&#x3D;反映出来</p>
<h4 id="遗传算法的步骤"><a href="#遗传算法的步骤" class="headerlink" title="遗传算法的步骤"></a>遗传算法的步骤</h4><ul>
<li>参数编码</li>
<li>初始种群的设定</li>
<li>适应度函数的设定</li>
<li>遗传变异操作</li>
<li>控制参数设定</li>
</ul>
<p>&#x3D;&#x3D;注意：控制参数设定&#x3D;&#x3D;</p>
<h4 id="分类"><a href="#分类" class="headerlink" title="分类"></a>分类</h4><h5 id="举例说明分类过程的两个阶段"><a href="#举例说明分类过程的两个阶段" class="headerlink" title="举例说明分类过程的两个阶段"></a>举例说明分类过程的两个阶段</h5><p>当一个幼儿学习橘子、苹果这两类事物时，我们可以给他提供 3 个橘子、两个苹果，并告诉他前 3 个对象是橘子、后 2 个是苹果，他通过观察果实的颜色、形状，以及品尝他们的味道来学习这两种事物，&#x3D;&#x3D;从而在大脑中建立起关于苹果和橘子的概念模型&#x3D;&#x3D;（分类模型生成阶段）<br>当再次看到之前未见到过的橘子和苹果时，通过建立起的分类模型，若能判断准确，说明已经掌握了这两个是事物的概念。（分类阶段）</p>
<h1 id="应用题"><a href="#应用题" class="headerlink" title="应用题"></a>应用题</h1><h2 id="知识表示"><a href="#知识表示" class="headerlink" title="知识表示"></a>知识表示</h2><h4 id="知识表示方法"><a href="#知识表示方法" class="headerlink" title="知识表示方法"></a>知识表示方法</h4><ul>
<li>一阶谓词逻辑知识表示法<br>(任意 x)[ROBOT (x) → COLOR (x,GRAY)]</li>
<li>产生式表示<br>老李年龄是 40 岁： （Li，age，40）<br>老李年龄很可能是 40 岁：（Li，age，40，0.8）</li>
<li>框架表示法</li>
</ul>
<h4 id="框架"><a href="#框架" class="headerlink" title="框架"></a>框架</h4><p>&#x3D;&#x3D;框架名 &lt;框架名&gt;&#x3D;&#x3D;<br>槽名 1：值 1<br>槽名 2：值 2<br>槽名 3：值 3<br>…………<br>槽名 n：值 n</p>
<p>例如：（课后题）<br>&#x3D;&#x3D;框架名 &lt;地震&gt;&#x3D;&#x3D;<br>日期：2008.08<br>地点：四川<br>震级：7.0<br>水含氧量：0.43<br>…………</p>
<p>框架式特点：结构性、继承性</p>
<h2 id="确定性推理"><a href="#确定性推理" class="headerlink" title="确定性推理"></a>确定性推理</h2><h4 id="化成子句集"><a href="#化成子句集" class="headerlink" title="化成子句集"></a>化成子句集</h4><p>&#x3D;&#x3D;一消二移三标准（使用两次）&#x3D;&#x3D;</p>
<p>第一次：消蕴含、等价符号，移否定，变量标准化<br>第二次：消存在量词，移全称量词，分配公式标准化</p>
<h4 id="归结原理求证结论"><a href="#归结原理求证结论" class="headerlink" title="归结原理求证结论"></a>归结原理求证结论</h4><p>三人面试问题<br>&#x3D;&#x3D;注意：NIL&#x3D;&#x3D;</p>
<h4 id="归结原理求解问题"><a href="#归结原理求解问题" class="headerlink" title="归结原理求解问题"></a>归结原理求解问题</h4><p>小李和小张的老师</p>
<p>&#x3D;&#x3D;注意：量词、结论否定析取答案&#x3D;&#x3D;</p>
<h2 id="不确定性推理"><a href="#不确定性推理" class="headerlink" title="不确定性推理"></a>不确定性推理</h2><h4 id="证据的不确定性"><a href="#证据的不确定性" class="headerlink" title="证据的不确定性"></a>证据的不确定性</h4><p>AND <strong>合取</strong> 求解&#x3D;&#x3D;min&#x3D;&#x3D;</p>
<p>OR <strong>析取</strong> 求解&#x3D;&#x3D;max&#x3D;&#x3D;</p>
<h4 id="求解结论的可信度"><a href="#求解结论的可信度" class="headerlink" title="求解结论的可信度"></a>求解结论的可信度</h4><p>&#x3D;&#x3D;CF（结论）&#x3D; CF（过程）+max（0，CF（条件））&#x3D;&#x3D;</p>
<p>CF（过程） 为 <em>可信度因子</em> ，取值范围 [ -1 , 1 ]</p>
<h4 id="证据理论"><a href="#证据理论" class="headerlink" title="证据理论"></a>证据理论</h4><h5 id="概率分配函数"><a href="#概率分配函数" class="headerlink" title="概率分配函数"></a>概率分配函数</h5><p>对于样本空间 D，任何一个子集都指派到 0 和 1 之间的一个数 M(A)，并且所有的子集的概率分配值为 1.</p>
<p>M(A)：A 的基本概率数</p>
<p>&#x3D;&#x3D;所有子集加和为 1&#x3D;&#x3D;</p>
<h5 id="信任函数"><a href="#信任函数" class="headerlink" title="信任函数"></a>信任函数</h5><p>Bel 函数 ，Bel(A) 对命题 A 为真的<strong>总的信任度</strong></p>
<h5 id="似然函数"><a href="#似然函数" class="headerlink" title="似然函数"></a>似然函数</h5><p>Pl(A) &#x3D; 1 - Bel(非 A)</p>
<h2 id="搜索"><a href="#搜索" class="headerlink" title="搜索"></a>搜索</h2><p><strong>BFS、DFS、启发式（3 选 2）</strong></p>
<p>open 表、closed 表、八数码问题</p>
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